aspire.denoising package¶
Submodules¶
aspire.denoising.adaptive_support module¶
- aspire.denoising.adaptive_support.adaptive_support(img_src, energy_threshold=0.99)¶
Determine size of the compact support in both real and Fourier Space.
Returns c_limit (support radius in Fourier space), and R_limit (support radius in real space).
Fourier c_limit is scaled in range [0, 0.5]. R_limit is in pixels [0, Image.resolution/2].
- Parameters:
img_src – Input Source of images.
energy_threshold – [0, 1] threshold limit
- Returns:
(c_limit, R_limit)
aspire.denoising.class_avg module¶
- class aspire.denoising.class_avg.ClassAvgSource(src, classifier, class_selector, averager)¶
Bases:
ImageSourceSource for denoised 2D images using class average methods.
Constructor of an object for denoising 2D images using class averaging methods.
- Parameters:
src – Source used for image classification.
classifier – Class2D subclass used for image classification. Example, RIRClass2D.
class_selector – A ClassSelector subclass.
averager – An Averager2D subclass.
- property class_distances¶
Returns table of class image distances as (src.n, n_nbors) Numpy array.
Follows same layout as class_indices but holds floats representing the distance (returned by classifier) to the zeroth image in each class.
Note n_nbors is managed by self.classifier and used here for documentation.
- Returns:
Numpy array, self.dtype.
- property class_indices¶
Returns table of class image indices as (src.n, n_nbors) Numpy array.
Each row reprsents a class, with the columns ordered by smallest class_distances from the reference image (zeroth columm).
Note n_nbors is managed by self.classifier and used here for documentation.
- Returns:
Numpy array, integers.
- property class_refl¶
Returns table of class image reflections as (src.n, n_nbors) Numpy array.
Follows same layout as class_indices but holds booleans that are True when the image should be reflected before averaging.
Note n_nbors is managed by self.classifier and used here for documentation.
- Returns:
Numpy array, boolean.
- property n¶
- save(*args, **kwargs)¶
Save metadata to STAR file.
See ImageSource.save for documentation.
- property selection_indices¶
- class aspire.denoising.class_avg.ClassAvgSourcev110(src, n_nbor=50, classifier=None, class_selector=None, averager=None, averager_src=None)¶
Bases:
ClassAvgSourceSource for denoised 2D images using class average methods.
Defaults to using Contrast based class selection (on the fly, compressed), avoiding neighbors of previous classes, and a brute force image alignment.
Currently this is the most reasonable default for experimental data.
Instantiates ClassAvgSourcev11 with the following parameters.
- Parameters:
src – Source used for image classification.
n_nbor – Number of nearest neighbors. Default 50.
classifier – Class2D classifier instance. Default None creates RIRClass2D. See code for parameter details.
class_selector – ClassSelector instance. Default None creates NeighborVarianceWithRepulsionClassSelector.
averager – Averager2D instance. Default None ceates BFSRAverager2D instance. See code for parameter details.
averager_src – Optionally explicitly assign source to BFSRAverager2D during initialization. Raises error when combined with an explicit averager argument.
- Returns:
ClassAvgSource instance.
- class aspire.denoising.class_avg.DebugClassAvgSource(src, n_nbor=10, classifier=None, class_selector=None, averager=None)¶
Bases:
ClassAvgSourceSource for denoised 2D images using class average methods.
- In this context Debug means defaulting to:
Using the defaults for RIRClass2D.
Using TopClassSelector to select all classes maintaining the same order as the input source.
Using BFRAverager2D with defaults on a single core.
Instantiates with default debug paramaters.
- Parameters:
src – Source used for image classification.
n_nbor – Number of nearest neighbors. Default 10.
classifier – Class2D classifier instance. Default None creates RIRClass2D. See code for parameter details.
class_selector – ClassSelector instance. Default None creates TopClassSelector.
averager – Averager2D instance. Default None ceates BFRAverager2D instance. See code for parameter details.
- Returns:
ClassAvgSource instance.
- aspire.denoising.class_avg.DefaultClassAvgSource(src, n_nbor=50, classifier=None, class_selector=None, averager=None, averager_src=None, version=None)¶
Source for denoised 2D images using class average methods.
Accepts version, to dispatch ClassAvgSource with parameters below. Default version is latest available.
- Parameters:
src – Source used for image classification.
n_nbor – Number of nearest neighbors. Default 50.
classifier – Class2D classifier instance. Default None creates RIRClass2D. See code for parameter details.
class_selector – ClassSelector instance. Default None creates NeighborVarianceWithRepulsionClassSelector.
averager – Averager2D instance. Default None ceates BFSRAverager2D instance. See code for parameter details.
averager_src – Optionally explicitly assign source to BFSRAverager2D during initialization. Raises error when combined with an explicit averager argument.
version – Optionally selects a versioned DefaultClassAvgSource. Defaults to latest available.
- Returns:
ClassAvgSource instance.
aspire.denoising.denoised_src module¶
- class aspire.denoising.denoised_src.DenoisedSource(src, denoiser)¶
Bases:
ImageSourceImageSource class serving denoised 2D images.
Initialize a denoised ImageSource object from an ImageSource.
- Parameters:
src – Original ImageSource object storing noisy images
denoiser – A Denoiser object for specifying a method for denoising
aspire.denoising.denoiser module¶
aspire.denoising.denoiser_cov2d module¶
- class aspire.denoising.denoiser_cov2d.DenoiserCov2D(src, basis=None, var_noise=None, batch_size=512, covar_opt=None)¶
Bases:
DenoiserDefine a derived class for denoising 2D images using Cov2D method
Initialize an object for denoising 2D images using Cov2D method
- Parameters:
src – The source object of 2D images with metadata
basis – The basis method to expand 2D images
var_noise – The estimated variance of noise
batch_size – Integer batch size for processing images. Defaults to 512.
covar_opt – Optional dictionary of option overides for Cov2D. Provided options will supersede defaults in DenoiserCov2D.default_opt.
- build_denoiser()¶
Build estimated mean and covariance matrix of 2D images.
This method should be computed once, on first images access.
- default_opt = {'iter_callback': [], 'max_iter': 250, 'rel_tolerance': 1e-12, 'shrinker': 'frobenius_norm', 'store_iterates': False, 'verbose': 0}¶
- aspire.denoising.denoiser_cov2d.src_wiener_coords(sim, mean_vol, eig_vols, lambdas=None, noise_var=0, batch_size=512)¶
Calculate coordinates using Wiener filter
- Parameters:
sim – A simulation object containing the images whose coordinates we want.
mean_vol – The mean volume of the source as Volume instance.
eig_vols – The eigenvolumes of the source as Volume instance.
lambdas – The eigenvalues in a K-by-K diagonal matrix (default eye(K)).
noise_var – The variance of the noise in the images (default 0).
batch_size – The size of the batches in which to compute the coordinates (default 512).
- Returns:
A K-by-src.n array of coordinates corresponding to the Wiener filter coordinates of each image in sim.
- The coordinates are obtained by the formula
alpha_s = eig_vols^T H_s ( y_s - P_s mean_vol ) ,
- where P_s is the forward image mapping and y_s is the sth image,
H_s = Sigma * P_s^T ( P_s Sigma P_s^T + noise_var I )^(-1) ,
and Sigma is the covariance matrix eig_vols * lambdas * eig_vols^T. Note that when noise_var is zero, this reduces to the projecting y_s onto the span of P_s eig_vols.
# TODO: Find a better place for this functionality other than in utils